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Mats Jirstrand

Head of Department

Sweden

Research Interests

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About

Mats Jirstrand is the Head of Department at the Fraunhofer-Chalmers Centre for Industrial Mathematics in Sweden. His research focuses on areas including hydronic balancing in residential heating systems, edge computing in connected vehicles, and pharmacokinetics of nanoparticles in cancer research. Additionally, he explores tumor growth models, economic control in district heating networks, metabolic responses to dietary challenges, and the application of convolutional neural networks in data analysis.

Articles (12)

Year:

2025

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2025

Year:

2024

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2024

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2024

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2023

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2023

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2023

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2023

Year:

2023

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2022

Year:

2020

Collaborators (4)

Wayne W. Campbell

Purdue University

UNITED STATES

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Diego Samuel Rodrigues

Professor Doutor MS-3.2

University of Campinas

BRAZIL

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Anders Esberg

Associated Professor

Umea University

SWEDEN

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Giuseppina Costabile

Università degli Studi di Napoli Federico II

ITALY

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Mats Jirstrand

Research Interests

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About

Articles (12)

Model-assisted hydronic balancing in residential heating systems using operational sensor data

The AutoSPADA platform: User-friendly edge computing for distributed learning and data analytics in connected vehicles

Accessing the pharmacokinetics of magnetic nanoparticles in cirrhosis-associated hepatocarcinogenesis by ordinary differential equation modeling and AC biosusceptometry

Effects on district heating networks by introducing demand side economic model predictive control

Probabilistic analysis of tumor growth inhibition models to Support trial design

Differential Responders to a Mixed Meal Tolerance Test Associated with Type 2 Diabetes Risk Factors and Gut Microbiota—Data from the MEDGI-Carb Randomized Controlled Trial

Analysis and Prediction of Postprandial Metabolic Response to Multiple Dietary Challenges Using Dynamic Mode Decomposition

Model‐based prediction of progression‐free survival for combination therapies in oncology

Utilization of convolutional neural networks for H I source finding

Online geometry assurance in individualized production by feedback control and model calibration of digital twins

A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

S-RASTER: contraction clustering for evolving data streams

Collaborators (4)

Wayne W. Campbell

Diego Samuel Rodrigues

Anders Esberg

Giuseppina Costabile

Make your next move.

Mats Jirstrand

Research Interests

Explore related searches

About

Articles (12)

Model-assisted hydronic balancing in residential heating systems using operational sensor data

The AutoSPADA platform: User-friendly edge computing for distributed learning and data analytics in connected vehicles

Accessing the pharmacokinetics of magnetic nanoparticles in cirrhosis-associated hepatocarcinogenesis by ordinary differential equation modeling and AC biosusceptometry

Effects on district heating networks by introducing demand side economic model predictive control

Probabilistic analysis of tumor growth inhibition models to Support trial design

Differential Responders to a Mixed Meal Tolerance Test Associated with Type 2 Diabetes Risk Factors and Gut Microbiota—Data from the MEDGI-Carb Randomized Controlled Trial

Analysis and Prediction of Postprandial Metabolic Response to Multiple Dietary Challenges Using Dynamic Mode Decomposition

Model‐based prediction of progression‐free survival for combination therapies in oncology

Utilization of convolutional neural networks for H I source finding

Online geometry assurance in individualized production by feedback control and model calibration of digital twins

A stochastic mixed effects model to assess treatment effects and fluctuations in home‐measured peak expiratory flow and the association with exacerbation risk in asthma

S-RASTER: contraction clustering for evolving data streams

Collaborators (4)

Wayne W. Campbell

Diego Samuel Rodrigues

Anders Esberg

Giuseppina Costabile

Make your next move.

Utilization of convolutional neural networks for H I source finding